{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TimeEval parameter optimization result analysis (Part 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import json\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pathlib import Path\n",
    "from timeeval import Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define data and results folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available result directories:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/projects/akita/results/2021-10-11_optim-part4'),\n",
       " PosixPath('/home/projects/akita/results/2021-09-27_shared-optim'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-06_optim-part1'),\n",
       " PosixPath('/home/projects/akita/results/2021-09-27_default-params-1&2&3&4-merged'),\n",
       " PosixPath('/home/projects/akita/results/.ipynb_checkpoints'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-08_optim-part3'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-07_optim-part2'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-12_optim-part5'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-12_optim-part6')]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Selecting:\n",
      "Data path: /home/projects/akita/data/test-cases\n",
      "Result path: /home/projects/akita/results/2021-10-11_optim-part4\n"
     ]
    }
   ],
   "source": [
    "# constants and configuration\n",
    "data_path = Path(\"/home/projects/akita/data\") / \"test-cases\"\n",
    "result_root_path = Path(\"/home/projects/akita/results\")\n",
    "experiment_result_folder = \"2021-10-11_optim-part4\"\n",
    "\n",
    "# build paths\n",
    "result_paths = [d for d in result_root_path.iterdir() if d.is_dir()]\n",
    "print(\"Available result directories:\")\n",
    "display(result_paths)\n",
    "\n",
    "result_path = result_root_path / experiment_result_folder\n",
    "print(\"\\nSelecting:\")\n",
    "print(f\"Data path: {data_path.resolve()}\")\n",
    "print(f\"Result path: {result_path.resolve()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load results and dataset metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading results from /home/projects/akita/results/2021-10-11_optim-part4\n"
     ]
    }
   ],
   "source": [
    "# load results\n",
    "print(f\"Reading results from {result_path.resolve()}\")\n",
    "df = pd.read_csv(result_path / \"results.csv\")\n",
    "\n",
    "# add dataset_name column\n",
    "df[\"dataset_name\"] = df[\"dataset\"].str.split(\".\").str[0]\n",
    "\n",
    "# load dataset metadata\n",
    "dmgr = Datasets(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract target optimized parameter names that were iterated in this run (per algorithm):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCI []\n",
      "PhaseSpace-SVM ['project_phasespace', 'nu', 'coef0', 'gamma', 'degree', 'kernel', 'tol']\n",
      "PST ['sim']\n",
      "Random Black Forest (RR) ['bootstrap']\n",
      "SAND ['alpha']\n"
     ]
    }
   ],
   "source": [
    "algo_param_mapping = {}\n",
    "algorithms = df[\"algorithm\"].unique()\n",
    "param_ignore_list = [\"max_anomaly_window_size\", \"anomaly_window_size\", \"neighbourhood_size\", \"window_size\", \"n_init_train\", \"embed_dim_range\"]\n",
    "\n",
    "for algo in algorithms:\n",
    "    param_sets = df.loc[df[\"algorithm\"] == algo, \"hyper_params\"].unique()\n",
    "    param_sets = [json.loads(ps) for ps in param_sets]\n",
    "    param_names = np.unique([name for ps in param_sets for name in ps if name not in param_ignore_list])\n",
    "    search_space = set()\n",
    "    for param_name in param_names:\n",
    "        values = []\n",
    "        for ps in param_sets:\n",
    "            try:\n",
    "                values.append(ps[param_name])\n",
    "            except:\n",
    "                pass\n",
    "        values = np.unique(values)\n",
    "        if values.shape[0] > 1:\n",
    "            search_space.add(param_name)\n",
    "    algo_param_mapping[algo] = list(search_space)\n",
    "\n",
    "for algo in algo_param_mapping:\n",
    "    print(algo, algo_param_mapping[algo])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract optimized parameters and their values (columns: optim_param_name and optim_param_value) for each experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_hyper_params(algo):\n",
    "    param_names = algo_param_mapping[algo]\n",
    "    def extract(value):\n",
    "        params = json.loads(value)\n",
    "        result = None\n",
    "        for name in param_names:\n",
    "            try:\n",
    "                value = params[name]\n",
    "                if isinstance(value, list):\n",
    "                    value = repr(value)\n",
    "                result = pd.Series([name, value], index=[\"optim_param_name\", \"optim_param_value\"])\n",
    "                break\n",
    "            except KeyError:\n",
    "                pass\n",
    "        if result is None:\n",
    "            return pd.Series([np.nan, np.nan], index=[\"optim_param_name\", \"optim_param_value\"])\n",
    "        return result\n",
    "    return extract\n",
    "\n",
    "df[[\"optim_param_name\", \"optim_param_value\"]] = \"\"\n",
    "for algo in algo_param_mapping:\n",
    "    df_algo = df.loc[df[\"algorithm\"] == algo]\n",
    "    df.loc[df_algo.index, [\"optim_param_name\", \"optim_param_value\"]] = df_algo[\"hyper_params\"].apply(extract_hyper_params(algo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract window size parameters (dependent params) and convert them into multiples of the dataset period size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dependent_param_names = [\"neighbourhood_size\", \"window_size\"]\n",
    "\n",
    "def extract_window_param(value, param_name=\"\"):\n",
    "    params = json.loads(value)\n",
    "    try:\n",
    "        return params[param_name]\n",
    "    except KeyError:\n",
    "        return 0\n",
    "\n",
    "for param_name in dependent_param_names:\n",
    "    s_windows = df[\"hyper_params\"].apply(extract_window_param, param_name=param_name)\n",
    "    df2 = df[s_windows > 0][[\"dataset\"]].copy()\n",
    "    df2[param_name] = s_windows[df2.index]\n",
    "    df2[\"period_size\"] = df2[\"dataset\"].apply(lambda d: dmgr.get((\"GutenTAG\", d)).period_size)\n",
    "    df2[\"optim_param_name\"] = param_name\n",
    "    df2[\"optim_param_value\"] = df2[param_name] / df2[\"period_size\"]\n",
    "    df2[\"optim_param_value\"] = (df2[\"optim_param_value\"]\n",
    "                                .fillna(df2[param_name])\n",
    "                                .round(1)\n",
    "                                .replace(50., 0.5)\n",
    "                                .replace(100, 1.0)\n",
    "                                .replace(150, 1.5)\n",
    "                                .replace(200, 2.0))\n",
    "    df.loc[df2.index, [\"optim_param_name\", \"optim_param_value\"]] = df2[[\"optim_param_name\", \"optim_param_value\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_scores_df(algorithm_name, dataset_id, optim_params, repetition=1):\n",
    "    params_id = df.loc[(df[\"algorithm\"] == algorithm_name) & (df[\"collection\"] == dataset_id[0]) & (df[\"dataset\"] == dataset_id[1]) & (df[\"optim_param_name\"] == optim_params[0]) & (df[\"optim_param_value\"] == optim_params[1]), \"hyper_params_id\"].item()\n",
    "    path = (\n",
    "        result_path /\n",
    "        algorithm_name /\n",
    "        params_id /\n",
    "        dataset_id[0] /\n",
    "        dataset_id[1] /\n",
    "        str(repetition) /\n",
    "        \"anomaly_scores.ts\"\n",
    "    )\n",
    "    return pd.read_csv(path, header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define plotting functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_use_plotly = True\n",
    "try:\n",
    "    import plotly.offline\n",
    "except ImportError:\n",
    "    default_use_plotly = False\n",
    "\n",
    "def plot_scores(algorithm_name, dataset_name, use_plotly: bool = default_use_plotly, **kwargs):\n",
    "    if isinstance(algorithm_name, tuple):\n",
    "        algorithms = [algorithm_name]\n",
    "    elif not isinstance(algorithm_name, list):\n",
    "        raise ValueError(\"Please supply a tuple (algorithm_name, optim_param_name, optim_param_value) or a list thereof as first argument!\")\n",
    "    else:\n",
    "        algorithms = algorithm_name\n",
    "    # construct dataset ID\n",
    "    dataset_id = (\"GutenTAG\", f\"{dataset_name}.unsupervised\")\n",
    "\n",
    "    # load dataset details\n",
    "    df_dataset = dmgr.get_dataset_df(dataset_id)\n",
    "\n",
    "    # check if dataset is multivariate\n",
    "    dataset_dim = df.loc[df[\"dataset_name\"] == dataset_name, \"dataset_input_dimensionality\"].unique().item()\n",
    "    dataset_dim = dataset_dim.lower()\n",
    "\n",
    "    auroc = {}\n",
    "    df_scores = pd.DataFrame(index=df_dataset.index)\n",
    "    skip_algos = []\n",
    "    algos = []\n",
    "    for algo, optim_param_name, optim_param_value in algorithms:\n",
    "        optim_params = f\"{optim_param_name}={optim_param_value}\"\n",
    "        algos.append((algo, optim_params))\n",
    "        # get algorithm metric results\n",
    "        try:\n",
    "            auroc[(algo, optim_params)] = df.loc[\n",
    "                (df[\"algorithm\"] == algo) & (df[\"dataset_name\"] == dataset_name) & (df[\"optim_param_name\"] == optim_param_name) & (df[\"optim_param_value\"] == optim_param_value),\n",
    "                \"ROC_AUC\"\n",
    "            ].item()\n",
    "        except ValueError:\n",
    "            warnings.warn(f\"No ROC_AUC score found! Probably {algo} with params {optim_params} was not executed on {dataset_name}.\")\n",
    "            auroc[(algo, optim_params)] = -1\n",
    "            skip_algos.append((algo, optim_params))\n",
    "            continue\n",
    "\n",
    "        # load scores\n",
    "        training_type = df.loc[df[\"algorithm\"] == algo, \"algo_training_type\"].values[0].lower().replace(\"_\", \"-\")\n",
    "        try:\n",
    "            df_scores[(algo, optim_params)] = load_scores_df(algo, (\"GutenTAG\", f\"{dataset_name}.{training_type}\"), (optim_param_name, optim_param_value)).iloc[:, 0]\n",
    "        except (ValueError, FileNotFoundError):\n",
    "            warnings.warn(f\"No anomaly scores found! Probably {algo} was not executed on {dataset_name} with params {optim_params}.\")\n",
    "            df_scores[(algo, optim_params)] = np.nan\n",
    "            skip_algos.append((algo, optim_params))\n",
    "    algorithms = [a for a in algos if a not in skip_algos]\n",
    "\n",
    "    if use_plotly:\n",
    "        return plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs)\n",
    "    else:\n",
    "        return plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs)\n",
    "\n",
    "def plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs):\n",
    "    import plotly.offline as py\n",
    "    import plotly.graph_objects as go\n",
    "    import plotly.figure_factory as ff\n",
    "    import plotly.express as px\n",
    "    from plotly.subplots import make_subplots\n",
    "\n",
    "    # Create plot\n",
    "    fig = make_subplots(2, 1)\n",
    "    if dataset_dim == \"multivariate\":\n",
    "        for i in range(1, df_dataset.shape[1]-1):\n",
    "            fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, i], name=f\"channel-{i}\"), 1, 1)\n",
    "    else:\n",
    "        fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, 1], name=\"timeseries\"), 1, 1)\n",
    "    fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset[\"is_anomaly\"], name=\"label\"), 2, 1)\n",
    "\n",
    "    for item in algorithms:\n",
    "        algo, optim_params = item\n",
    "        fig.add_trace(go.Scatter(x=df_scores.index, y=df_scores[item], name=f\"{algo}={auroc[item]:.4f} ({optim_params})\"), 2, 1)\n",
    "    fig.update_xaxes(matches=\"x\")\n",
    "    fig.update_layout(\n",
    "        title=f\"Results of {','.join(np.unique([a for a, _ in algorithms]))} on {dataset_name}\",\n",
    "        height=400\n",
    "    )\n",
    "    return py.iplot(fig)\n",
    "\n",
    "def plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs):\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    # Create plot\n",
    "    fig, axs = plt.subplots(2, 1, sharex=True, figsize=(20, 8))\n",
    "    if dataset_dim == \"multivariate\":\n",
    "        for i in range(1, df_dataset.shape[1]-1):\n",
    "            axs[0].plot(df_dataset.index, df_dataset.iloc[:, i], label=f\"channel-{i}\")\n",
    "    else:\n",
    "        axs[0].plot(df_dataset.index, df_dataset.iloc[:, 1], label=f\"timeseries\")\n",
    "    axs[1].plot(df_dataset.index, df_dataset[\"is_anomaly\"], label=\"label\")\n",
    "\n",
    "    for item in algorithms:\n",
    "        algo, optim_params = item\n",
    "        axs[1].plot(df_scores.index, df_scores[item], label=f\"{algo}={auroc[item]:.4f} ({optim_params})\")\n",
    "    axs[0].legend()\n",
    "    axs[1].legend()\n",
    "    fig.suptitle(f\"Results of {','.join(np.unique([a for a, _ in algorithms]))} on {dataset_name}\")\n",
    "    fig.tight_layout()\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">PR_AUC</th>\n",
       "      <th colspan=\"3\" halign=\"left\">ROC_AUC</th>\n",
       "      <th>train_main_time</th>\n",
       "      <th>execute_main_time</th>\n",
       "      <th>repetition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algorithm</th>\n",
       "      <th>optim_param_name</th>\n",
       "      <th>optim_param_value</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">SAND</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">alpha</th>\n",
       "      <th>0.5</th>\n",
       "      <td>0.001166</td>\n",
       "      <td>0.511353</td>\n",
       "      <td>0.602111</td>\n",
       "      <td>0.167172</td>\n",
       "      <td>0.893100</td>\n",
       "      <td>0.984021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.869954</td>\n",
       "      <td>168</td>\n",
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       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>0.000960</td>\n",
       "      <td>0.500990</td>\n",
       "      <td>0.652231</td>\n",
       "      <td>0.137892</td>\n",
       "      <td>0.886059</td>\n",
       "      <td>0.974030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.869422</td>\n",
       "      <td>168</td>\n",
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       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>0.001138</td>\n",
       "      <td>0.466757</td>\n",
       "      <td>0.451481</td>\n",
       "      <td>0.171241</td>\n",
       "      <td>0.877221</td>\n",
       "      <td>0.975811</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.768892</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Random Black Forest (RR)</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">bootstrap</th>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"26\" valign=\"top\">PhaseSpace-SVM</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">tol</th>\n",
       "      <th>0.0001</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.250308</td>\n",
       "      <td>0.078643</td>\n",
       "      <td>0.042545</td>\n",
       "      <td>0.699250</td>\n",
       "      <td>0.715774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.862911</td>\n",
       "      <td>168</td>\n",
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       "    <tr>\n",
       "      <th>1e-05</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.250308</td>\n",
       "      <td>0.078643</td>\n",
       "      <td>0.042545</td>\n",
       "      <td>0.699250</td>\n",
       "      <td>0.715774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.191451</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.001</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.250308</td>\n",
       "      <td>0.078643</td>\n",
       "      <td>0.042545</td>\n",
       "      <td>0.699250</td>\n",
       "      <td>0.715774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.273794</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.250309</td>\n",
       "      <td>0.078642</td>\n",
       "      <td>0.042541</td>\n",
       "      <td>0.699250</td>\n",
       "      <td>0.715778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.422068</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.250314</td>\n",
       "      <td>0.078646</td>\n",
       "      <td>0.042554</td>\n",
       "      <td>0.699246</td>\n",
       "      <td>0.715765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.170109</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">project_phasespace</th>\n",
       "      <th>1.0</th>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.331567</td>\n",
       "      <td>0.302878</td>\n",
       "      <td>0.000324</td>\n",
       "      <td>0.785048</td>\n",
       "      <td>0.917337</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.790783</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.250308</td>\n",
       "      <td>0.078643</td>\n",
       "      <td>0.042545</td>\n",
       "      <td>0.699250</td>\n",
       "      <td>0.715774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.012330</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">nu</th>\n",
       "      <th>0.35</th>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.268596</td>\n",
       "      <td>0.108972</td>\n",
       "      <td>0.044715</td>\n",
       "      <td>0.714049</td>\n",
       "      <td>0.765970</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.055168</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.250308</td>\n",
       "      <td>0.078643</td>\n",
       "      <td>0.042545</td>\n",
       "      <td>0.699250</td>\n",
       "      <td>0.715774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.241439</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.65</th>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.235793</td>\n",
       "      <td>0.069432</td>\n",
       "      <td>0.041576</td>\n",
       "      <td>0.673619</td>\n",
       "      <td>0.699350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.562894</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">kernel</th>\n",
       "      <th>rbf</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.250308</td>\n",
       "      <td>0.078643</td>\n",
       "      <td>0.042545</td>\n",
       "      <td>0.699250</td>\n",
       "      <td>0.715774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.055219</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.089779</td>\n",
       "      <td>0.026930</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.491283</td>\n",
       "      <td>0.503715</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119.215743</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigmoid</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.071046</td>\n",
       "      <td>0.015358</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.489251</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.864614</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poly</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.092252</td>\n",
       "      <td>0.015755</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.465959</td>\n",
       "      <td>0.501543</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.833202</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rbf-exp</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rbf-gaussian</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">gamma</th>\n",
       "      <th>0.8</th>\n",
       "      <td>0.003049</td>\n",
       "      <td>0.514748</td>\n",
       "      <td>0.625272</td>\n",
       "      <td>0.181718</td>\n",
       "      <td>0.906465</td>\n",
       "      <td>0.981766</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.392127</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.003298</td>\n",
       "      <td>0.521041</td>\n",
       "      <td>0.630332</td>\n",
       "      <td>0.171500</td>\n",
       "      <td>0.906313</td>\n",
       "      <td>0.979419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.233708</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.001337</td>\n",
       "      <td>0.492244</td>\n",
       "      <td>0.554234</td>\n",
       "      <td>0.208033</td>\n",
       "      <td>0.895805</td>\n",
       "      <td>0.978541</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.486092</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">degree</th>\n",
       "      <th>2.0</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.250308</td>\n",
       "      <td>0.078643</td>\n",
       "      <td>0.042545</td>\n",
       "      <td>0.699250</td>\n",
       "      <td>0.715774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.662527</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.250308</td>\n",
       "      <td>0.078643</td>\n",
       "      <td>0.042545</td>\n",
       "      <td>0.699250</td>\n",
       "      <td>0.715774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.175690</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.250308</td>\n",
       "      <td>0.078643</td>\n",
       "      <td>0.042545</td>\n",
       "      <td>0.699250</td>\n",
       "      <td>0.715774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.690384</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">coef0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.250308</td>\n",
       "      <td>0.078643</td>\n",
       "      <td>0.042545</td>\n",
       "      <td>0.699250</td>\n",
       "      <td>0.715774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.575879</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.250308</td>\n",
       "      <td>0.078643</td>\n",
       "      <td>0.042545</td>\n",
       "      <td>0.699250</td>\n",
       "      <td>0.715774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.347524</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.250308</td>\n",
       "      <td>0.078643</td>\n",
       "      <td>0.042545</td>\n",
       "      <td>0.699250</td>\n",
       "      <td>0.715774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.293446</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.250308</td>\n",
       "      <td>0.078643</td>\n",
       "      <td>0.042545</td>\n",
       "      <td>0.699250</td>\n",
       "      <td>0.715774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.279026</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">PST</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">window_size</th>\n",
       "      <th>1.0</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.338418</td>\n",
       "      <td>0.168211</td>\n",
       "      <td>0.016049</td>\n",
       "      <td>0.796201</td>\n",
       "      <td>0.858833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.712785</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.360014</td>\n",
       "      <td>0.149425</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.787393</td>\n",
       "      <td>0.857580</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.851303</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.279240</td>\n",
       "      <td>0.154775</td>\n",
       "      <td>0.081858</td>\n",
       "      <td>0.781458</td>\n",
       "      <td>0.834913</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.578679</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.362407</td>\n",
       "      <td>0.138420</td>\n",
       "      <td>0.000232</td>\n",
       "      <td>0.780039</td>\n",
       "      <td>0.852107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.846926</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">sim</th>\n",
       "      <th>simn</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>simo</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">PCI</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">window_size</th>\n",
       "      <th>0.5</th>\n",
       "      <td>0.001966</td>\n",
       "      <td>0.270538</td>\n",
       "      <td>0.131389</td>\n",
       "      <td>0.022453</td>\n",
       "      <td>0.690935</td>\n",
       "      <td>0.657851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.146975</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.001483</td>\n",
       "      <td>0.236636</td>\n",
       "      <td>0.106440</td>\n",
       "      <td>0.020760</td>\n",
       "      <td>0.673814</td>\n",
       "      <td>0.609082</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.305325</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>0.001056</td>\n",
       "      <td>0.221018</td>\n",
       "      <td>0.081814</td>\n",
       "      <td>0.012254</td>\n",
       "      <td>0.666941</td>\n",
       "      <td>0.607497</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.360593</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.001065</td>\n",
       "      <td>0.216193</td>\n",
       "      <td>0.077094</td>\n",
       "      <td>0.009204</td>\n",
       "      <td>0.664313</td>\n",
       "      <td>0.616359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.577021</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                 PR_AUC  \\\n",
       "                                                                    min   \n",
       "algorithm                optim_param_name   optim_param_value             \n",
       "SAND                     alpha              0.5                0.001166   \n",
       "                                            0.1                0.000960   \n",
       "                                            0.9                0.001138   \n",
       "Random Black Forest (RR) bootstrap          0                       NaN   \n",
       "                                            1.0                     NaN   \n",
       "PhaseSpace-SVM           tol                0.0001             0.000116   \n",
       "                                            1e-05              0.000116   \n",
       "                                            0.001              0.000116   \n",
       "                                            0.01               0.000116   \n",
       "                                            0.1                0.000116   \n",
       "                         project_phasespace 1.0                0.000151   \n",
       "                                            0                  0.000116   \n",
       "                         nu                 0.35               0.000127   \n",
       "                                            0.5                0.000116   \n",
       "                                            0.65               0.000106   \n",
       "                         kernel             rbf                0.000116   \n",
       "                                            linear             0.000050   \n",
       "                                            sigmoid            0.000050   \n",
       "                                            poly               0.000050   \n",
       "                                            rbf-exp                 NaN   \n",
       "                                            rbf-gaussian            NaN   \n",
       "                         gamma              0.8                0.003049   \n",
       "                                            1.0                0.003298   \n",
       "                                            0.5                0.001337   \n",
       "                         degree             2.0                0.000116   \n",
       "                                            3                  0.000116   \n",
       "                                            4                  0.000116   \n",
       "                         coef0              0                  0.000116   \n",
       "                                            1.0                0.000116   \n",
       "                                            10                 0.000116   \n",
       "                                            100                0.000116   \n",
       "PST                      window_size        1.0                0.000050   \n",
       "                                            1.5                0.000057   \n",
       "                                            0.5                0.000050   \n",
       "                                            2.0                0.000056   \n",
       "                         sim                simn                    NaN   \n",
       "                                            simo                    NaN   \n",
       "PCI                      window_size        0.5                0.001966   \n",
       "                                            1.0                0.001483   \n",
       "                                            1.5                0.001056   \n",
       "                                            2.0                0.001065   \n",
       "\n",
       "                                                                         \\\n",
       "                                                                   mean   \n",
       "algorithm                optim_param_name   optim_param_value             \n",
       "SAND                     alpha              0.5                0.511353   \n",
       "                                            0.1                0.500990   \n",
       "                                            0.9                0.466757   \n",
       "Random Black Forest (RR) bootstrap          0                       NaN   \n",
       "                                            1.0                     NaN   \n",
       "PhaseSpace-SVM           tol                0.0001             0.250308   \n",
       "                                            1e-05              0.250308   \n",
       "                                            0.001              0.250308   \n",
       "                                            0.01               0.250309   \n",
       "                                            0.1                0.250314   \n",
       "                         project_phasespace 1.0                0.331567   \n",
       "                                            0                  0.250308   \n",
       "                         nu                 0.35               0.268596   \n",
       "                                            0.5                0.250308   \n",
       "                                            0.65               0.235793   \n",
       "                         kernel             rbf                0.250308   \n",
       "                                            linear             0.089779   \n",
       "                                            sigmoid            0.071046   \n",
       "                                            poly               0.092252   \n",
       "                                            rbf-exp                 NaN   \n",
       "                                            rbf-gaussian            NaN   \n",
       "                         gamma              0.8                0.514748   \n",
       "                                            1.0                0.521041   \n",
       "                                            0.5                0.492244   \n",
       "                         degree             2.0                0.250308   \n",
       "                                            3                  0.250308   \n",
       "                                            4                  0.250308   \n",
       "                         coef0              0                  0.250308   \n",
       "                                            1.0                0.250308   \n",
       "                                            10                 0.250308   \n",
       "                                            100                0.250308   \n",
       "PST                      window_size        1.0                0.338418   \n",
       "                                            1.5                0.360014   \n",
       "                                            0.5                0.279240   \n",
       "                                            2.0                0.362407   \n",
       "                         sim                simn                    NaN   \n",
       "                                            simo                    NaN   \n",
       "PCI                      window_size        0.5                0.270538   \n",
       "                                            1.0                0.236636   \n",
       "                                            1.5                0.221018   \n",
       "                                            2.0                0.216193   \n",
       "\n",
       "                                                                         \\\n",
       "                                                                 median   \n",
       "algorithm                optim_param_name   optim_param_value             \n",
       "SAND                     alpha              0.5                0.602111   \n",
       "                                            0.1                0.652231   \n",
       "                                            0.9                0.451481   \n",
       "Random Black Forest (RR) bootstrap          0                       NaN   \n",
       "                                            1.0                     NaN   \n",
       "PhaseSpace-SVM           tol                0.0001             0.078643   \n",
       "                                            1e-05              0.078643   \n",
       "                                            0.001              0.078643   \n",
       "                                            0.01               0.078642   \n",
       "                                            0.1                0.078646   \n",
       "                         project_phasespace 1.0                0.302878   \n",
       "                                            0                  0.078643   \n",
       "                         nu                 0.35               0.108972   \n",
       "                                            0.5                0.078643   \n",
       "                                            0.65               0.069432   \n",
       "                         kernel             rbf                0.078643   \n",
       "                                            linear             0.026930   \n",
       "                                            sigmoid            0.015358   \n",
       "                                            poly               0.015755   \n",
       "                                            rbf-exp                 NaN   \n",
       "                                            rbf-gaussian            NaN   \n",
       "                         gamma              0.8                0.625272   \n",
       "                                            1.0                0.630332   \n",
       "                                            0.5                0.554234   \n",
       "                         degree             2.0                0.078643   \n",
       "                                            3                  0.078643   \n",
       "                                            4                  0.078643   \n",
       "                         coef0              0                  0.078643   \n",
       "                                            1.0                0.078643   \n",
       "                                            10                 0.078643   \n",
       "                                            100                0.078643   \n",
       "PST                      window_size        1.0                0.168211   \n",
       "                                            1.5                0.149425   \n",
       "                                            0.5                0.154775   \n",
       "                                            2.0                0.138420   \n",
       "                         sim                simn                    NaN   \n",
       "                                            simo                    NaN   \n",
       "PCI                      window_size        0.5                0.131389   \n",
       "                                            1.0                0.106440   \n",
       "                                            1.5                0.081814   \n",
       "                                            2.0                0.077094   \n",
       "\n",
       "                                                                ROC_AUC  \\\n",
       "                                                                    min   \n",
       "algorithm                optim_param_name   optim_param_value             \n",
       "SAND                     alpha              0.5                0.167172   \n",
       "                                            0.1                0.137892   \n",
       "                                            0.9                0.171241   \n",
       "Random Black Forest (RR) bootstrap          0                       NaN   \n",
       "                                            1.0                     NaN   \n",
       "PhaseSpace-SVM           tol                0.0001             0.042545   \n",
       "                                            1e-05              0.042545   \n",
       "                                            0.001              0.042545   \n",
       "                                            0.01               0.042541   \n",
       "                                            0.1                0.042554   \n",
       "                         project_phasespace 1.0                0.000324   \n",
       "                                            0                  0.042545   \n",
       "                         nu                 0.35               0.044715   \n",
       "                                            0.5                0.042545   \n",
       "                                            0.65               0.041576   \n",
       "                         kernel             rbf                0.042545   \n",
       "                                            linear             0.000000   \n",
       "                                            sigmoid            0.000100   \n",
       "                                            poly               0.000000   \n",
       "                                            rbf-exp                 NaN   \n",
       "                                            rbf-gaussian            NaN   \n",
       "                         gamma              0.8                0.181718   \n",
       "                                            1.0                0.171500   \n",
       "                                            0.5                0.208033   \n",
       "                         degree             2.0                0.042545   \n",
       "                                            3                  0.042545   \n",
       "                                            4                  0.042545   \n",
       "                         coef0              0                  0.042545   \n",
       "                                            1.0                0.042545   \n",
       "                                            10                 0.042545   \n",
       "                                            100                0.042545   \n",
       "PST                      window_size        1.0                0.016049   \n",
       "                                            1.5                0.000454   \n",
       "                                            0.5                0.081858   \n",
       "                                            2.0                0.000232   \n",
       "                         sim                simn                    NaN   \n",
       "                                            simo                    NaN   \n",
       "PCI                      window_size        0.5                0.022453   \n",
       "                                            1.0                0.020760   \n",
       "                                            1.5                0.012254   \n",
       "                                            2.0                0.009204   \n",
       "\n",
       "                                                                         \\\n",
       "                                                                   mean   \n",
       "algorithm                optim_param_name   optim_param_value             \n",
       "SAND                     alpha              0.5                0.893100   \n",
       "                                            0.1                0.886059   \n",
       "                                            0.9                0.877221   \n",
       "Random Black Forest (RR) bootstrap          0                       NaN   \n",
       "                                            1.0                     NaN   \n",
       "PhaseSpace-SVM           tol                0.0001             0.699250   \n",
       "                                            1e-05              0.699250   \n",
       "                                            0.001              0.699250   \n",
       "                                            0.01               0.699250   \n",
       "                                            0.1                0.699246   \n",
       "                         project_phasespace 1.0                0.785048   \n",
       "                                            0                  0.699250   \n",
       "                         nu                 0.35               0.714049   \n",
       "                                            0.5                0.699250   \n",
       "                                            0.65               0.673619   \n",
       "                         kernel             rbf                0.699250   \n",
       "                                            linear             0.491283   \n",
       "                                            sigmoid            0.489251   \n",
       "                                            poly               0.465959   \n",
       "                                            rbf-exp                 NaN   \n",
       "                                            rbf-gaussian            NaN   \n",
       "                         gamma              0.8                0.906465   \n",
       "                                            1.0                0.906313   \n",
       "                                            0.5                0.895805   \n",
       "                         degree             2.0                0.699250   \n",
       "                                            3                  0.699250   \n",
       "                                            4                  0.699250   \n",
       "                         coef0              0                  0.699250   \n",
       "                                            1.0                0.699250   \n",
       "                                            10                 0.699250   \n",
       "                                            100                0.699250   \n",
       "PST                      window_size        1.0                0.796201   \n",
       "                                            1.5                0.787393   \n",
       "                                            0.5                0.781458   \n",
       "                                            2.0                0.780039   \n",
       "                         sim                simn                    NaN   \n",
       "                                            simo                    NaN   \n",
       "PCI                      window_size        0.5                0.690935   \n",
       "                                            1.0                0.673814   \n",
       "                                            1.5                0.666941   \n",
       "                                            2.0                0.664313   \n",
       "\n",
       "                                                                         \\\n",
       "                                                                 median   \n",
       "algorithm                optim_param_name   optim_param_value             \n",
       "SAND                     alpha              0.5                0.984021   \n",
       "                                            0.1                0.974030   \n",
       "                                            0.9                0.975811   \n",
       "Random Black Forest (RR) bootstrap          0                       NaN   \n",
       "                                            1.0                     NaN   \n",
       "PhaseSpace-SVM           tol                0.0001             0.715774   \n",
       "                                            1e-05              0.715774   \n",
       "                                            0.001              0.715774   \n",
       "                                            0.01               0.715778   \n",
       "                                            0.1                0.715765   \n",
       "                         project_phasespace 1.0                0.917337   \n",
       "                                            0                  0.715774   \n",
       "                         nu                 0.35               0.765970   \n",
       "                                            0.5                0.715774   \n",
       "                                            0.65               0.699350   \n",
       "                         kernel             rbf                0.715774   \n",
       "                                            linear             0.503715   \n",
       "                                            sigmoid            0.500000   \n",
       "                                            poly               0.501543   \n",
       "                                            rbf-exp                 NaN   \n",
       "                                            rbf-gaussian            NaN   \n",
       "                         gamma              0.8                0.981766   \n",
       "                                            1.0                0.979419   \n",
       "                                            0.5                0.978541   \n",
       "                         degree             2.0                0.715774   \n",
       "                                            3                  0.715774   \n",
       "                                            4                  0.715774   \n",
       "                         coef0              0                  0.715774   \n",
       "                                            1.0                0.715774   \n",
       "                                            10                 0.715774   \n",
       "                                            100                0.715774   \n",
       "PST                      window_size        1.0                0.858833   \n",
       "                                            1.5                0.857580   \n",
       "                                            0.5                0.834913   \n",
       "                                            2.0                0.852107   \n",
       "                         sim                simn                    NaN   \n",
       "                                            simo                    NaN   \n",
       "PCI                      window_size        0.5                0.657851   \n",
       "                                            1.0                0.609082   \n",
       "                                            1.5                0.607497   \n",
       "                                            2.0                0.616359   \n",
       "\n",
       "                                                              train_main_time  \\\n",
       "                                                                         mean   \n",
       "algorithm                optim_param_name   optim_param_value                   \n",
       "SAND                     alpha              0.5                           NaN   \n",
       "                                            0.1                           NaN   \n",
       "                                            0.9                           NaN   \n",
       "Random Black Forest (RR) bootstrap          0                             NaN   \n",
       "                                            1.0                           NaN   \n",
       "PhaseSpace-SVM           tol                0.0001                        NaN   \n",
       "                                            1e-05                         NaN   \n",
       "                                            0.001                         NaN   \n",
       "                                            0.01                          NaN   \n",
       "                                            0.1                           NaN   \n",
       "                         project_phasespace 1.0                           NaN   \n",
       "                                            0                             NaN   \n",
       "                         nu                 0.35                          NaN   \n",
       "                                            0.5                           NaN   \n",
       "                                            0.65                          NaN   \n",
       "                         kernel             rbf                           NaN   \n",
       "                                            linear                        NaN   \n",
       "                                            sigmoid                       NaN   \n",
       "                                            poly                          NaN   \n",
       "                                            rbf-exp                       NaN   \n",
       "                                            rbf-gaussian                  NaN   \n",
       "                         gamma              0.8                           NaN   \n",
       "                                            1.0                           NaN   \n",
       "                                            0.5                           NaN   \n",
       "                         degree             2.0                           NaN   \n",
       "                                            3                             NaN   \n",
       "                                            4                             NaN   \n",
       "                         coef0              0                             NaN   \n",
       "                                            1.0                           NaN   \n",
       "                                            10                            NaN   \n",
       "                                            100                           NaN   \n",
       "PST                      window_size        1.0                           NaN   \n",
       "                                            1.5                           NaN   \n",
       "                                            0.5                           NaN   \n",
       "                                            2.0                           NaN   \n",
       "                         sim                simn                          NaN   \n",
       "                                            simo                          NaN   \n",
       "PCI                      window_size        0.5                           NaN   \n",
       "                                            1.0                           NaN   \n",
       "                                            1.5                           NaN   \n",
       "                                            2.0                           NaN   \n",
       "\n",
       "                                                              execute_main_time  \\\n",
       "                                                                           mean   \n",
       "algorithm                optim_param_name   optim_param_value                     \n",
       "SAND                     alpha              0.5                       53.869954   \n",
       "                                            0.1                       53.869422   \n",
       "                                            0.9                       53.768892   \n",
       "Random Black Forest (RR) bootstrap          0                               NaN   \n",
       "                                            1.0                             NaN   \n",
       "PhaseSpace-SVM           tol                0.0001                    62.862911   \n",
       "                                            1e-05                     68.191451   \n",
       "                                            0.001                     63.273794   \n",
       "                                            0.01                      64.422068   \n",
       "                                            0.1                       61.170109   \n",
       "                         project_phasespace 1.0                       63.790783   \n",
       "                                            0                         65.012330   \n",
       "                         nu                 0.35                      46.055168   \n",
       "                                            0.5                       62.241439   \n",
       "                                            0.65                      77.562894   \n",
       "                         kernel             rbf                       62.055219   \n",
       "                                            linear                   119.215743   \n",
       "                                            sigmoid                   50.864614   \n",
       "                                            poly                      46.833202   \n",
       "                                            rbf-exp                         NaN   \n",
       "                                            rbf-gaussian                    NaN   \n",
       "                         gamma              0.8                      102.392127   \n",
       "                                            1.0                      101.233708   \n",
       "                                            0.5                       96.486092   \n",
       "                         degree             2.0                       63.662527   \n",
       "                                            3                         65.175690   \n",
       "                                            4                         65.690384   \n",
       "                         coef0              0                         60.575879   \n",
       "                                            1.0                       61.347524   \n",
       "                                            10                        62.293446   \n",
       "                                            100                       64.279026   \n",
       "PST                      window_size        1.0                       20.712785   \n",
       "                                            1.5                       27.851303   \n",
       "                                            0.5                       13.578679   \n",
       "                                            2.0                       33.846926   \n",
       "                         sim                simn                            NaN   \n",
       "                                            simo                            NaN   \n",
       "PCI                      window_size        0.5                        9.146975   \n",
       "                                            1.0                        5.305325   \n",
       "                                            1.5                        5.360593   \n",
       "                                            2.0                        5.577021   \n",
       "\n",
       "                                                              repetition  \n",
       "                                                                   count  \n",
       "algorithm                optim_param_name   optim_param_value             \n",
       "SAND                     alpha              0.5                      168  \n",
       "                                            0.1                      168  \n",
       "                                            0.9                      168  \n",
       "Random Black Forest (RR) bootstrap          0                        193  \n",
       "                                            1.0                      193  \n",
       "PhaseSpace-SVM           tol                0.0001                   168  \n",
       "                                            1e-05                    168  \n",
       "                                            0.001                    168  \n",
       "                                            0.01                     168  \n",
       "                                            0.1                      168  \n",
       "                         project_phasespace 1.0                      168  \n",
       "                                            0                        168  \n",
       "                         nu                 0.35                     168  \n",
       "                                            0.5                      168  \n",
       "                                            0.65                     168  \n",
       "                         kernel             rbf                      168  \n",
       "                                            linear                   168  \n",
       "                                            sigmoid                  168  \n",
       "                                            poly                     168  \n",
       "                                            rbf-exp                  168  \n",
       "                                            rbf-gaussian             168  \n",
       "                         gamma              0.8                      168  \n",
       "                                            1.0                      168  \n",
       "                                            0.5                      168  \n",
       "                         degree             2.0                      168  \n",
       "                                            3                        168  \n",
       "                                            4                        168  \n",
       "                         coef0              0                        168  \n",
       "                                            1.0                      168  \n",
       "                                            10                       168  \n",
       "                                            100                      168  \n",
       "PST                      window_size        1.0                      168  \n",
       "                                            1.5                      168  \n",
       "                                            0.5                      168  \n",
       "                                            2.0                      168  \n",
       "                         sim                simn                     168  \n",
       "                                            simo                     168  \n",
       "PCI                      window_size        0.5                      168  \n",
       "                                            1.0                      168  \n",
       "                                            1.5                      168  \n",
       "                                            2.0                      168  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_by = (\"ROC_AUC\", \"mean\")\n",
    "metric_agg_type = [\"min\", \"mean\", \"median\"]\n",
    "time_agg_type = \"mean\"\n",
    "aggs = {\n",
    "    \"PR_AUC\": metric_agg_type,\n",
    "    \"ROC_AUC\": metric_agg_type,\n",
    "    \"train_main_time\": time_agg_type,\n",
    "    \"execute_main_time\": time_agg_type,\n",
    "    \"repetition\": \"count\"\n",
    "}\n",
    "\n",
    "df_tmp = df.reset_index()\n",
    "df_tmp = df_tmp.groupby(by=[\"algorithm\", \"optim_param_name\", \"optim_param_value\"]).agg(aggs)\n",
    "df_tmp = df_tmp.reset_index()\n",
    "df_tmp = df_tmp.sort_values(by=[\"algorithm\", \"optim_param_name\", sort_by], ascending=False)\n",
    "df_tmp = df_tmp.set_index([\"algorithm\", \"optim_param_name\", \"optim_param_value\"])\n",
    "\n",
    "with pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n",
    "    display(df_tmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selected parameters\n",
    "\n",
    "- SAND: **Check why it failed on so many datasets!**\n",
    "- Random Black Forest (RR): **Only TIMEOUTs** --> perform search over n_estimators, n_trees, and bootstrap again\n",
    "- PhaseSpace-SVM: `coef0=0,tol=0.001,project_phasespace=True,gamma=1.0,nu=0.35,degree=3,kernel=\"rbf\"` (tol, coef0: make no difference; nu: smaller is better; gamma: 0.8 is very slightly better for ROC_AUC, but worse for PR_AUC; degree: makes no difference)\n",
    "- PST: `window_size=\"1.0 * dataset period size\",sim=` **wrong parameter format** `sim` is case-sensitive!\n",
    "- PCI: `window_size=\"0.5 * dataset period size\"` (smaller is better)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status.OK</th>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              dataset\n",
       "status               \n",
       "Status.ERROR      120\n",
       "Status.OK         384"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"algorithm\"] == \"SAND\"].groupby(by=[\"status\"])[[\"dataset\"]].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <th>hyper_params</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Status.TIMEOUT</th>\n",
       "      <th>{\"bootstrap\": false, \"max_depth\": 4, \"max_features_method\": \"auto\", \"max_features_per_estimator\": 0.5, \"min_samples_leaf\": 1, \"min_samples_split\": 2, \"n_estimators\": 500, \"n_jobs\": 1, \"n_trees\": 500, \"random_state\": 42, \"train_window_size\": 500, \"verbose\": 0}</th>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{\"bootstrap\": true, \"max_depth\": 4, \"max_features_method\": \"auto\", \"max_features_per_estimator\": 0.5, \"min_samples_leaf\": 1, \"min_samples_split\": 2, \"n_estimators\": 500, \"n_jobs\": 1, \"n_trees\": 500, \"random_state\": 42, \"train_window_size\": 500, \"verbose\": 0}</th>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   dataset\n",
       "status         hyper_params                                               \n",
       "Status.TIMEOUT {\"bootstrap\": false, \"max_depth\": 4, \"max_featu...      193\n",
       "               {\"bootstrap\": true, \"max_depth\": 4, \"max_featur...      193"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"algorithm\"] == \"Random Black Forest (RR)\"].groupby(by=[\"status\", \"hyper_params\"])[[\"dataset\"]].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores([\n",
    "    (\"PST\", \"window_size\", 1),\n",
    "    (\"PST\", \"window_size\", 1.5)\n",
    "], \"rw-combined-diff-2\", use_plotly=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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